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Why Enterprise Data Strategy (EDS)?

By   /  April 18, 2016  /  No Comments

Click here to learn more about author Assad Shaik.

With focus on financial industry

What exactly prompts organizations to adopt an enterprise data strategy? Why is it essential for an organization to define an enterprise data strategy?

On a quick look, it is evident that most of the organizations struggle to understand the importance and the value of data, and thus fail to implement an EDS. Most companies end up focusing and investing more on IT applications, and they ignore the fact that data flows through these applications. Finally, they end up creating redundant applications, redundant data stores, and processes that are broken and complex. This makes it difficult for an organization to fetch the best out of their own resources.

Speaking broadly, there are three primary business drivers that necessitate organizations to define an enterprise data strategy. Let’s discuss each of them in detail.

Key Business Drivers Responsible For Defining an EDS

  • To explore and discover more revenue generating mechanics

Profits, productivity and efficiency are the three main pillars that an organization primarily looks to achieve, on a long-term basis. So how can the revenue be optimized and further enhanced? An organization definitely needs to be more customer-centric. A successful organization is one that can understand the tastes and preferences of its customers. An organization must be well acquainted with the customer behaviors, and it needs to put the best shoe forward by launching products and/or services entitled to fetch the customer interests and thus generate profit. But how do you get so close to your customer to achieve this useful insight? Obviously, data is the right answer.

A proper strategy must be implemented such that an optimized process can be enabled. This process will aid in managing the customer life cycle. With the well-planned strategy, customer information can be efficiently captured at record origination. Further, related historical and unstructured data can be stored in active data storage capabilities for large scale analytics. Finally, companies can establish a single version of truth capability. Social circles and networks that customers have say volumes about their interests. This collected data can help you boost your profits and could help you get close to your customers – thus generating revenue. This is why an Enterprise Data Structure is required.

  • To meet the necessary regulatory compliances

An Enterprise Data Structure is not only required to boost profits, but it has other important roles as well. Having a clear and well-formulated Enterprise Data Structure will help your organization be in accordance with the necessary regulatory requirements. Financial institutions need to comply with the stringent regulatory frameworks to capital adequacy, stress testing and market liquidity risk. These organizations also need to practice due-diligence to detect the suspicious activities, including the predicate offenses for money laundering and terrorist activities.

In recent times, many financial institutions received MRAs and high penalties for not complying with the OCC’s standards. Enterprise Data Strategy has become the highest priority for these organizations as it ensures the data standards in accordance with OCC specifications.

  • Maintaining a pace with evolving business strategies

Businesses are undergoing huge changes with the diversification and development of technology. FinTech, Digitization and Cognitively intelligent services are the booming terms used rapidly throughout the industry, and a large number of the organizations have already started to strategize and transform in this direction.

To successfully leverage these capabilities, complete, accurate and reliable data is needed. This data might arrive from a variety of channels, and it can be structured or unstructured, high volumes or high speed. This necessitates the need to define and execute a data strategy which, in turn, lays the foundation to enable an organization’s vision of transforming to sophisticated technical capabilities

About the author

Assad M. Shaik, Chief Data Scientist Assad M. Shaik is a well-known Chief Data Scientist with international repute for his expertise in the areas of large scale data processing, data management and data mining. Mr. Shaik is acclaimed and well respected in the emerging areas of Big Data and large scale Machine Learning, especially in fields of Bayesian Learning and Markov Chains. As a practitioner with extensive industry expertise in Banking and Financial Markets, Mr. Shaik currently holds a critical position of essential capacity in a large national bank with a distinguished reputation. He has also formerly served in other executive positions at large multinationals. Mr.Shaik is also a prolific author and speaker in the field of data science and data mining. He has authored books and has original publications in cutting edge industry techniques in the areas of Master Data Management and Customer Journeys. Connect with Assad on LinkedIn

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